In addition, Precision, and Recall metrics [1]} are reported in Table REF . The precision measures the proportion of generated samples that are close to the real dataset in VGG feature space [2]}, and the recall measures the proportion of real dataset that are close to generated samples in VGG feature space. We note that models with better results often have higher precision and lower recall. We expect our method to increase precision as it refines the generated distribution to better match the target distribution. But since our task aims at finding a subset of the source distribution, our method theoretically cannot increase the recall: increasing recall would require synthesizing new modes of real data without access to any new real examples. In our setting, the ideal maximizes precision while maintaining recall unchanged from the pre-trained model. A drop in recall reveals some loss in diversity, and measures headroom for improving upon our method.
